Understanding the customer experience and your brand’s reputation are some of the major benefits to be derived from using big data, making it one of the most valuable resources to tap into for all businesses in the 21st century.

One way to start mining the masses of data available at your fingertips is through sentiment analysis. For example, by analyzing thousands of product reviews, you could discover how customers feel about your pricing plan or customer service levels.

Monitoring brand sentiment on social media in real-time, as well as over time, could lead to the detection of disgruntled customers,enabling you to course-correct and react earlier.

What is sentiment analysis?

Also known as opinion mining or emotion AI, sentiment analysis includes identifying consumer attitudes, emotions, and opinions of your company’s product, brand or service online. It can be defined as the ‘computational treatment’ of opinion, sentiment, and subjectivity in text.

In basic terms, it is the process of classifying online text as either positive, negative or neutral. It could also focus on revealing feelings, attitudes and emotions, such as angry, happy and sad, as well as urgency and non-urgency and intentions such as interested or not interested.

The purpose is to interpret and classify these subjective opinions using natural language processing (NLP) and machine learning algorithms. By analyzing so-called unstructured data at scale, such as customer reviews and social media conversations, businesses are better able to listen to their customers and make informed decisions relating to their products and services.

See a more detailed definition of sentiment analysis and its benefits.

To learn more about the basics of sentiment analysis, please download this free report.

Use cases of sentiment analysis

Sentiment analysis is used in different ways and can therefore be applied to countless aspects of business, from brand monitoring and product analytics to customer service and market research. Imagine being able to consume and analyze 100,000 free text customer reviews to identify key themes and the sentiment attached to those themes in a matter of hours without injecting any human interpretation bias. Find more information about the use cases of sentiment analysis.

Types of sentiment analysis

While there are various types of sentiment analysis approaches, those with the highest level of adoption include fine-grained sentiment analysis, emotion detection, aspect-based sentiment analysis, and intent analysis. Read an explanation of the different types of sentiment analysis.

Get started with sentiment analysis

Sentiment analysis is vast and could seem overwhelming at first. The first question, as with the adaptation of any new business tool or technique, is to determine the goals of sentiment analysis and, specifically, which business questions you are trying to answer. This will enable you to define and tailor the type, model, and tools of sentiment analysis best suited to your business needs.

There are essentially two major methods of sentiment analysis:

1. Rule-based
2. Automatic

Rule-based

This is based on an algorithm with a clearly manually defined description of an opinion or set of rules to identify in an online text. It usually involves a basic Natural Language Processing (NLP) routine – which means the algorithm goes through the text and finds the predefined words that match the set criteria for example: ‘great’ equals positive or ‘bad’ as negative and then calculates which types of words are more prevalent in the text. If there are more positive words – for example – the text will be declared to have a positive polarity. Rule-based sentiment analysis is simple, but it is limited as it doesn’t take context into account. It can be used for more generic purposes – for example, to work out the tone of messages, which could be helpful when it comes to customer support. One example of rule-based sentiment analysis: A seller on the Amazon marketplace could use a sentiment model to quickly assess thousands of reviews and gauge customer satisfaction with their goods.

Datasets

There are many evaluations and labeled sentiment datasets that have been created, especially for Twitter posts and Amazon product reviews to choose from, for example:

• Sentiment Strength Twitter Dataset
• SemEval (Semantic Evaluation) dataset

You can also use the APIs provided by many platforms such as Twitter to crawl and collect data.

Automatic or machine learning

Machine learning methods are used for large-scale sentiment analysis and go much deeper than rule-based sentiment analysis – essentially it uses machine learning to figure out the sentiments or gist of the message. This method is more accurate and precise and various criteria can be used to process the information. This is particularly useful for brands that actively engage with customers for example on social media or live chat, which it can be difficult to tell the sentiment behind a message. Sentiment analysis helps brands learn more about customer perception using qualitative feedback. Choosing the right tools for your needs when it comes to sentiment analysis is crucial as the wrong tool could lead to excessive costs and inaccurate results. The 2020 Hubspot list of ‘Best sentiment analysis tools’ is a good starting point for assessing the pros and cons of various tools for your business needs. The cost associated with training staff to use the tools effectively should be factored into your business expenses. Alternatively, to avoid time and money spent on training internally, consider hiring a technology partner to help navigate these options.

Metrics or reports

How you measure the efficacy of the sentiment analysis you applied will be determined by the tools chosen and could include metrics such as precision, recall, F-score and accuracy.

Data Visualization

How best to visualize the results of sentiment analysis to present this information effectively is a critical consideration to ensure the correct interpretation and future usefulness of the results. Word clouds, interactive maps, graphs and histograms are a few examples of the data visualization techniques commonly used.

Sentiment analysis is becoming an essential element in the business toolkit in the 21st century, but choosing the correct goals, methods and tools for sentiment analysis is crucial to ensure maximum benefit to your business.

To learn more about the basics of sentiment analysis, please download this free report and feel free to reach us.

Let our data experts at REEA Global show you how to get started with sentiment analysis, today! Give us a shout — we’re ready to help you hit the ground running